A computational approach for mapping electrochemical activity of multi-principal element alloys
Files
(Published version)
Date
2023
Authors
Yuwono, J.A.
Li, X.
Doležal, T.D.
Samin, A.J.
Shi, J.Q.
Li, Z.
Birbilis, N.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
npj Materials Degradation, 2023; 7(1):87-1-87-11
Statement of Responsibility
Jodie A. Yuwono, Xinyu Li, Tyler D. Doležal, Adib J. Samin, Javen Qinfeng Shi, Zhipeng Li, and Nick Birbilis
Conference Name
Abstract
Multi principal element alloys (MPEAs) comprise an atypical class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations favours a computational alloy design approach. In order to computationally assess the prospective corrosion performance of MPEA, an approach was developed in this study. A density functional theory (DFT) – based Monte Carlo method was used for the development of MPEA ‘structure’; with the AlCrTiV alloy used as a model. High-throughput DFT calculations were performed to create training datasets for surface activity/selectivity towards different adsorbate species: O2-, Cl- and H+. Machine-learning (ML) with combined representation was then utilised to predict the adsorption and vacancy energies as descriptors for surface activity/ selectivity. The capability of the combined computational methods of MC, DFT and ML, as a virtual electrochemical performance simulator for MPEAs was established and may be useful in exploring other MPEAs.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http:// creativecommons.org/licenses/by/4.0/.